CN103575689B - Method for rapidly detecting amylose content in rice by near infrared spectrum and visible light analyzer - Google Patents
Method for rapidly detecting amylose content in rice by near infrared spectrum and visible light analyzer Download PDFInfo
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Abstract
The invention discloses a method for rapidly detecting the amylose content in rice by near infrared spectrum and a visible light analyzer. The method comprises the following steps: S1: collecting rice samples; S2: determining the amylose content in rice by adopting a standard method; S3: collecting near infrared spectrum data of the samples by utilizing a near infrared spectroscopy; S4: pretreating the near infrared spectrum data; S5: correlating the near infrared spectrum data with an amylose value determined by the standard method, and establishing a near infrared calibration model; S6: carrying out external verification on the near infrared calibration model; S7: correlating the amylose value determined by the standard method with a color value determined by a visible light analyzer by utilizing a predicted value obtained by the near infrared calibration model, and establishing a comprehensive calibration model; and S8: carrying out external verification on the comprehensive verifying model. The method provided by the invention has the advantages of high analyzing speed, high efficiency, low cost, no pollution to the environment and the like, and the method can provide reliable basis for the rice quality analysis and rice quality control.
Description
Technical field
The present invention designs a kind of method utilizing near infrared spectrum and visible light analysis instrument to detect amylose content in rice fast.
Background technology
Whole world rice total production 90% for direct human consumption, and directly related with consumer be the edible of rice and nutritional quality.Amylose is as the food flavor, expansivity, the water-intake rate that affect rice, and the color of rice, gloss, the characteristic such as viscosity and hardness determine the quality of rice.Amylose content adopts colorimetric method for determining usually, and measures a sample and at least need several hours, is unsuitable for the amylose content measuring rice in enormous quantities.Therefore need to find a kind of method accurately detecting amylose content in rice fast, for the attributional analysis of rice and quality control provide reliable basis.
In recent years, the application of near-infrared spectrum technique in the field such as chemical industry, agricultural is very extensive.In crops, near infrared technology can detect moisture, the protein content of rice fast, but also fails to replace classic method to measure on measurement rice grain amylose content.Jap.P. 11-304698 discloses a kind of method that near infrared spectrum detects polished rice amylose content, and the method is just set up aspect from model and improved the accuracy rate detecting polished rice amylose, fails to replace the amylose content of traditional technique in measuring polished rice.Chinese patent ZL01140315.2 discloses a kind of method and the device thereof that cereal are carried out to Quality Detection, what adopt is the near infrared light spectrum information of 890 nanometer to 1100 nanometers, the content of the compositions such as protein, grease, starch and moisture in main detection cereal, but through experimental study, the content only using near infrared light to detect starch shows that accuracy rate is not high, be difficult to replace classic method.Explored by applicant, study discovery, the accurate fast of amylose content in rice can be realized in conjunction with near infrared spectrum and visible light analysis instrument and detect.
Summary of the invention
The object of this invention is to provide a kind of method utilizing near infrared spectrum and visible light analysis instrument to detect amylose content in rice.
The present invention is to provide a kind of method utilizing near infrared spectrum and visible light analysis instrument to detect amylose content in rice, comprise the steps:
S1: collect Rice Samples three groups, first group is used for setting up near infrared correction; Second group is used for setting up integrated calibration model; 3rd group for verifying the accuracy of near infrared correction and integrated calibration model;
S2: adopt standard method to measure the content of the amylose in rice;
S3: utilize near infrared spectrometer to gather the near infrared spectrum data of collected sample;
S4: the pre-service of near infrared spectrum;
S5: the near infrared spectrum data of each sample in first group of rice is associated with the amylose value measured with standard method, utilizes chemo metric software to set up near infrared correction;
S6: the three group of Rice Samples carries out external certificate near infrared correction;
S7: the amylose value that second group of Rice Samples is measured with standard method, the color value that the predicted value utilizing near infrared correction to obtain and visible light analysis instrument record is associated, and sets up integrated calibration model with chemo metric software;
S8: the three group of Rice Samples carries out external certificate to integrated calibration model.
Automatic analyzer is utilized to detect the content of amylose in rice in above-mentioned steps S2;
Near-infrared wavelength described in above-mentioned steps S3 is 850-1045nm, and with near infrared spectrometer in rice scanning step, scan mode is continuous wavelength infrared diaphanoscopy; Spectral information used is absorbance;
In above-mentioned steps S4 near infrared spectrum pre-treatment step, preprocess method is second order Method of Seeking Derivative;
The near infrared spectrum data of each sample in first group of rice is associated with the amylose value measured with standard method by above-mentioned steps S5, utilizes the partial least square method (PLS) in Unscrambler V9.5 software to set up near infrared correction;
Carry out external certificate with the 3rd group of rice near infrared correction in above-mentioned steps S6, the accuracy of checking near infrared correction, need before checking to carry out pre-service to the spectrum of this group rice, preprocess method is with step S4;
The amylose value that second group of rice measures with standard method by above-mentioned steps S7, the presentation quality of the predicted value utilizing near infrared correction to obtain and the single grain of rice that utilizes visible light analysis instrument ES-1000 to record is associated, and utilizes the arithmetic of linearity regression (MLR) of Unscrambler V.9.5 in software to set up integrated calibration model:
AC＝b
0+b
1x
1+b
2x
2+b
3x
3+b
4x
4+b
5x
5+b
6x
6+b
7x
7+b
8x
8+b
9x
9+b
10x
10+b
11x
11+b
12x
12+b
13x
13+b
14x
14+b
15x
15
Wherein: AC represents amylose content, b
0constant term, b
jfor the discriminant coefficient of jth (j=1,2 ..., 15) individual discrimination variable, x
1the predicted value that the second group of rice described in step S7 utilizes the near infrared correction in step S5 to obtain, x
2to x
15the color value that visible light analysis instrument records, x
2: RG0, be red/green reflectivity, x
3: RG3, red/green transmissivity, x
4: the color phase average of Hue, end face and bottom surface, x
5: the degree of Chroma, brightness, x
6: Whiteness, change whiteness signal from reflectivity to transmissivity, x
7: R1, detect the red light reflectance of the grain of rice from end face, x
8: G1, detect the green light reflectance of the grain of rice from end face, x
9: B1, detect the blue light reflectance of the grain of rice from end face, x
10: R2, detect the red light reflectance of the grain of rice from bottom surface, x
11: G2, detect the green light reflectance of the grain of rice from bottom surface, x
12: B2, detect the blue light reflectance of the grain of rice from bottom surface, x
13: the transmissivity of the ruddiness of R3, the detection grain of rice, x
14: the transmissivity of the green glow of G3, the detection grain of rice, x
15: the transmissivity of the blue light of B3, the detection grain of rice;
The present invention is to provide the method utilizing near infrared spectrum and visible light analysis instrument to measure amylose content in rice, the method is quick, accurate, simple to operate.Whole test process only needed less than 2 minutes, and the method directly measuring a sample amylose content in rice needs at least 2 hours.
Accompanying drawing explanation
Fig. 1 is the process flow diagram utilizing near infrared spectrum and visible light analysis instrument to detect amylose content in rice fast;
Fig. 2 is that first group of rice is through pretreated near infrared light spectrogram;
Fig. 3 is the relation scatter diagram between the 3rd group of Rice Samples predicted value that utilizes integrated calibration model to calculate and the measured value utilizing standard method to measure.
Specific implementation method
The embodiment utilizing near infrared spectrum and visible light analysis instrument to detect the method for amylose content in rice fast is below provided.
Embodiment 1:
Step S1: collect Rice Samples three groups;
First group: the Rice Samples collecting 2008 to 2010 results, amount to 325 samples and set up near infrared correction; Integrated calibration model set up by 111 samples of results in second group: 2010; 3rd group: 2009,57 sample checking near-infrared models of results in 2010 and the accuracy of integrated calibration model;
Step S2: adopt the amylose content in automatic analyzer mensuration rice;
The mensuration of amylose content adopts automatic analyzer to measure; The measurement result of the amylose content of final all samples is as following table:
Step S3: utilize near infrared spectrometer to gather the near infrared spectrum data of rice;
The collection of spectroscopic data uses the BR-5000 near infrared spectrometer of company of Shizu Seiki K.K.; First open BR-5000 near infrared spectrometer and preheating, get 550ml Rice Samples and pour near infrared spectrometer entrance into; Adopt continuous wavelength infrared diaphanoscopy to gather spectrum, scanning Spectral range is 850-1045nm, at interval of 2nm run-down; In order to overcome spectral drift that sample difference causes, reduce error, each sample repeats experiment 3 times, samples absorbance in this spectrum as spectral information data;
Step S4: the near infrared spectrum pre-service of rice: adopt second order differentiate disposal route to carry out pre-service to the sample spectrum that step S3 obtains;
Step S5: the near infrared spectrum data of each sample in first group of rice be associated with the amylose value measured with standard method, utilizes the partial least square method (PLS) in Unscrambler V9.5 software to set up near infrared correction;
Step S6: external certificate is carried out near infrared correction with the 3rd group of rice, the accuracy of checking near infrared correction, need before checking to carry out pre-service to the spectrum of this group rice, preprocess method is with step S4, and the result is as follows:
R
2: the coefficient of determination; The mean value of Bias: the three group of predicted value that rice utilizes near infrared correction to obtain and measured value deviation; SEP: the three group of standard error that rice utilizes near infrared correction to obtain, RPD: relation analysis error; RPD is used for confirming the practicality of model, as follows in concrete utilization: 0.0 to 2.3 show that model can not be used, 2.4 to 3.0 show that model can only carry out very rough screening, 3.1 to 4.9 show that model can be used for screening different sample but be only applicable to laboratory study aspect, 5.0 to 6.4 show that model can well screen rice, show that model can replace the content of amylose in the traditional technique in measuring rice of standard, RPD value in this example is 3.91, shows that this model only can be used for laboratory study aspect;
The amylose value that step S7: the second group of rice adopts standard method to measure, the color value that the predicted value utilizing near infrared correction to obtain and visible light analysis instrument ES-1000 record is associated, utilize the arithmetic of linearity regression (MLR) in Unscrambler V9.5 software to set up integrated calibration model, formula is as follows:
AC=93.070+0.772x
1one 0.001x
2+ 0.017x
3-0.027x
4+ 0.183x
5-0.028x
6-0.028x
7+ 0.158x
8-0.122x
9-0.181x
10+ 0.099x
11+ 0.042x
12-0.047x
13+ 0.001x
14+ 0.019x
15
AC represents amylose content, x
1the predicted value that the second group of rice described in step S7 utilizes the near infrared correction in step S5 to calculate, x
2to x
15the color value that visible light analysis instrument records, x
2: RG0, be red/green reflectivity, x
3: RG3, red/green transmissivity, x
4: the color phase average of Hue, end face and bottom surface, x
5: the degree of Chroma, brightness, x
6: Whiteness, change whiteness signal from reflectivity to transmissivity, x
7: R1, detect the red light reflectance of the grain of rice from end face, x
8: G1, detect the green light reflectance of the grain of rice from end face, x
9: B1, detect the blue light reflectance of the grain of rice from end face, x
10: R2, detect the red light reflectance of the grain of rice from bottom surface, x
11: G2, detect the green light reflectance of the grain of rice from bottom surface, x
12: B2, detect the blue light reflectance of the grain of rice from bottom surface, x
13: the red transmission rate of R3, the detection grain of rice, x
14: the transmissivity of the green glow of G3, the detection grain of rice, x
15: the transmissivity of the blue light of B3, the detection grain of rice;
Step S8: the three group of Rice Samples carries out external certificate to integrated calibration model, and result is as follows:
RPD value is greater than 5, shows that integrated calibration model can replace the content of amylose in traditional technique in measuring rice.Analysis speed of the present invention is fast, efficiency is high, cost is low and do not cause the features such as any pollution to environment, can be the attributional analysis of rice, controls rice quality and provide reliable basis.
Claims (1)
1. detect a method for amylose content in rice fast with near infrared spectrum and visible light analysis instrument, its concrete steps are:
S1: collect Rice Samples three groups, first group is used for setting up near infrared correction; Second group is used for setting up integrated calibration model; 3rd group for verifying the accuracy of near infrared correction and integrated calibration model;
S2: adopt standard method to measure the content of amylose in rice;
S3: utilize near infrared spectrometer to gather the near infrared spectrum of collected sample;
S4: the pre-service of near infrared spectrum;
S5: the near infrared spectrum data of each sample in first group of rice is associated with the amylose value measured with standard method, utilizes the partial least square method in chemo metric software Unscrambler V9.5 to set up near infrared correction;
S6: the three group of Rice Samples carries out external certificate near infrared correction;
S7: the amylose value that second group of Rice Samples is measured with standard method, the color value that the predicted value utilizing near infrared correction to obtain and visible light analysis instrument record is associated, and sets up integrated calibration model with the arithmetic of linearity regression in chemo metric software Unscrambler V9.5:
AC＝b
0+b
1x
1+b
2x
2+b
3x
3+b
4x
4+b
5x
5+b
6x
6+b
7x
7+b
8x
8+b
9x
9+b
10x
10+b
11x
11+b
12x
12+b
13x
13+b
14x
14+b
15x
15
Wherein: AC represents amylose content, b
0constant term, b
jfor the discriminant coefficient of a jth discrimination variable, j=1,2 ..., 15, x
1the predicted value that the second group of rice described in step S7 utilizes the near infrared correction in step S5 to calculate, x
2to x
15the color value that visible light analysis instrument records, x
2: RG0, red/green reflectivity, x
3: RG3, red/green transmissivity, x
4: the color phase average of Hue, end face and bottom surface, x
5: the degree of Chroma, brightness, x
6: Whiteness, change whiteness signal from reflectivity to transmissivity, x
7: R1, detect the red light reflectance of the grain of rice from end face, x
8: G1, detect the green light reflectance of the grain of rice from end face, x
9: B1, detect the blue light reflectance of the grain of rice from end face, x
10: R2, detect the red light reflectance of the grain of rice from bottom surface, x
11: G2, detect the green light reflectance of the grain of rice from bottom surface, x
12: B2, detect the blue light reflectance of the grain of rice from bottom surface, x
13: the transmissivity of the ruddiness of R3, the detection grain of rice, x
14: the transmissivity of the green glow of G3, the detection grain of rice, x
15: the transmissivity of the blue light of B3, the detection grain of rice;
S8: the three group of Rice Samples carries out external certificate to integrated calibration model.
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| CN105136791A (en) * | 2015-09-17 | 2015-12-09 | 阜阳师范学院 | Preparation method of test paper for quickly determining content of amylose in flour |
| CN105181643B (en) * | 2015-10-12 | 2018-03-02 | 华中农业大学 | A kind of near infrared detection method of rice quality and application |
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| CN112683842A (en) * | 2020-11-23 | 2021-04-20 | 河南工业大学 | Method for measuring total starch content and starch direct-to-branch ratio of wheat in infrared spectrum interval |
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